Arterial spin labeling (ASL) (Detre et al., 1992) is a non-invasive and non-contrast perfusion imaging method for magnetic resonance imaging (MRI). By deriving perfusion data from the natural magnetic resonance (MR) signal of magnetically tagged blood, instead of from an extrinsic contrast agent, ASL provides a safer option for assessing tissue perfusion in patients at risk of nephrogenic systemic fibrosis (Hasebroock and Serkova, 2009) and a preferable option in other situations, such as when imaging infants and children. Cerebral blood flow (CBF) maps estimated using ASL are consistent with dynamic susceptibility contrast (DSC) results and have been widely applied to cerebrovascular studies, such as stroke (Wang et al., 2012) and Alzheimer's disease (Yoshiura et al., 2009). ASL is also preferable for studies requiring repeated perfusion assessments in a short period of time, such as functional MRI (Borogovac and Asllani, 2012; Detre and Wang, 2002). The absolute quantification of blood flow in ASL directly reveals physiological changes (Asllani et al., 2009; Rusinek et al., 2010).
Since the blood magnetization is “labeled” upstream of the volume of interest, a portion of the ASL signal decays before arterial blood flows into the imaging slab, and the acquired signal thus depends on the tagged blood arrival time, called the arterial transit time (ATT), which in turn depends on both the blood flow velocity and the distance between the tagging plane and the imaged region. However, most ASL studies follow the single post label delay (PLD) protocol, which results in a “static” 2D/3D ASL image. It cannot provide a subject-dependent ATT map and requires a simplified ASL model, which may result in errors in CBF quantification (Dai et al., 2012; Qiu et al., 2010). A PLD longer than the ATT can ensure the blood bolus has flowed into surrounding tissue and reduces estimation error (Alsop and Detre, 1996), but this method may miss the peak ASL signal and requires prior knowledge of the ATT.
A multiple-PLD protocol can measure multiple phases of ASL perfusion and fully characterize the ASL dynamic model. It can improve CBF accuracy and provide rich hemodynamic information, such as ATT for characterizing cerebrovascular diseases (Macintosh et al., 2012). But, the intrinsically low signal-to-noise ratio (SNR) of ASL can require extensive signal averaging, and thus a multiple-PLD protocol may become prohibitively time-consuming.
Dynamic ASL imaging can be accelerated in two ways: (1) acquiring undersampled k-space data, and (2) reducing the number of averages. However, accelerating ASL data acquisition in this way comes at the cost of reduced image quality and CBF accuracy using conventional techniques.
It is with respect to these and other considerations that the various embodiments described below are presented.